code: def _execute():
      #################### Environment ####################
      env = Environment(
          train_dataset=get_breast_cancer_data(target="target"),
          results_path="HyperparameterHunterAssets",
          metrics=["roc_auc_score"],
          cv_type="StratifiedKFold",
          cv_params=dict(n_splits=5, shuffle=True, random_state=32),
      )
  
      #################### Experimentation ####################
      experiment = CVExperiment(
          model_initializer=KerasClassifier,
          model_init_params=dict(build_fn=_build_fn_experiment),
          model_extra_params=dict(
              callbacks=[ReduceLROnPlateau(patience=5)],
              batch_size=32,
              epochs=10,
              verbose=0,
          ),
      )
  
      #################### Optimization ####################
      optimizer = BayesianOptPro(iterations=10)
      optimizer.forge_experiment(
          model_initializer=KerasClassifier,
          model_init_params=dict(build_fn=_build_fn_optimization),
          model_extra_params=dict(
              callbacks=[ReduceLROnPlateau(patience=Integer(5, 10))],
              batch_size=Categorical([32, 64], transform="onehot"),
              epochs=10,
              verbose=0,
          ),
      )
      optimizer.go()
  
file_path: hyperparameter_hunter/examples/keras_examples/optimization_example.py
imports: from hyperparameter_hunter import Environment, CVExperiment, BayesianOptPro
  from hyperparameter_hunter import Real, Integer, Categorical
  from hyperparameter_hunter.utils.learning_utils import get_breast_cancer_data
  import os.path
  from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau
  from keras.layers import Dense, Activation, Dropout
  from keras.models import Sequential
  from keras.wrappers.scikit_learn import KerasClassifier
label_desc: Use image node_id 3 for calling a global function with example usage: _execute() without return types